main.py

from pydantic import Field
from speckle_automate import AutomationContext, AutomateBase, execute_automate_function
from Utilities.helpers import flatten_base, speckle_print

from rules import RevitRules


from Utilities.helpers import flatten_base, speckle_print
from Utilities.spreadsheet import read_rules_from_spreadsheet
from Workshop.Exercise_4.rules import apply_rules_to_objects


class FunctionInputs(AutomateBase):
    """These are function author defined values.

    Automate will make sure to supply them matching the types specified here.
    Please use the pydantic model schema to define your inputs:
    https://docs.pydantic.dev/latest/usage/models/
    """

    # In this exercise, we will move rules to an external source so not to hardcode them.
    spreadsheet_url: str = Field(
        title="Spreadsheet URL",
        description="This is the URL of the spreadsheet to check. It should be a TSV format data source.",
    )


def automate_function(
    automate_context: AutomationContext,
    function_inputs: FunctionInputs,
) -> None:
    """This version of the function will add a check for the new provide inputs.

    Args:
        automate_context: A context helper object, that carries relevant information
            about the runtime context of this function.
            It gives access to the Speckle project data, that triggered this run.
            It also has convenience methods attach result data to the Speckle model.
        function_inputs: An instance object matching the defined schema.
    """

    # the context provides a convenient way, to receive the triggering version
    version_root_object = automate_context.receive_version()

    # We can continue to work with a flattened list of objects.
    flat_list_of_objects = list(flatten_base(version_root_object))

    # read the rules from the spreadsheet
    rules = read_rules_from_spreadsheet(function_inputs.spreadsheet_url)

    # apply the rules to the objects
    apply_rules_to_objects(flat_list_of_objects, rules, automate_context)

    # set the automation context view, to the original model / version view
    automate_context.set_context_view()

    # report success
    automate_context.mark_run_success(
        f"Successfully applied rules to {len(flat_list_of_objects)} objects."
    )



# make sure to call the function with the executor
if __name__ == "__main__":
    # NOTE: always pass in the automate function by its reference, do not invoke it!

    # pass in the function reference with the inputs schema to the executor
    execute_automate_function(automate_function, FunctionInputs)